[1]} propose three novel modeling techniques to address the unique challenges of text summarization.
First, a feature-rich encoder is developed to identify key concepts and entities that are crucial to capture the main idea and content of the source documents. As illustrated in Figure REF  (Left), in addition to word embeddings, the encoder captures rich linguistic features, including parts-of-speech (POS) tags, named-entity (NE) tags, and TF and IDF statistics of words.
